System and method for sample evaluation

ABSTRACT

In variants, a method for analog product determination can include: determining functional property feature values for a target and determining variable values for a prototype based on the functional property feature values for the target.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Application No. 18/098,898filed Jan. 19, 2023, which claims the benefit of U.S. ProvisionalApplication No. 63/301,948 filed Jan. 21, 2021, each of which isincorporated in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the food science field, and morespecifically to a new and useful system and method in the food sciencefield.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a variant of the method.

FIG. 2 depicts an example of determining functional property featurevalues.

FIGS. 3A-3D depict examples of determining variable values.

FIG. 4A depicts a first example of training a characterization model(e.g., a characterization model depicted in FIG. 3A and/or FIG. 3B).

FIG. 4B depicts a second example of training a characterization model(e.g., a characterization model depicted in FIG. 3B).

FIG. 5A depicts a first example of training a variable value model(e.g., a variable value model depicted in FIG. 3A).

FIG. 5B depicts a second example of training a variable value model(e.g., a variable value model depicted in FIG. 3D).

FIG. 5C depicts a third example of training a variable value model(e.g., a variable value model depicted in FIG. 3D).

FIG. 6 depicts an illustrative example of determining variable valuesfor a prototype.

FIG. 7 depicts an example of extracting prototype functional propertyfeature values and target functional property feature values.

FIG. 8 depicts a first illustrative example of a comparison metric.

FIG. 9A depicts a second illustrative example of a comparison metric.

FIG. 9B depicts a third illustrative example of a comparison metric.

DETAILED DESCRIPTION

The following description of the embodiments of the invention is notintended to limit the invention to these embodiments, but rather toenable any person skilled in the art to make and use this invention.

1. Overview

As shown in FIG. 1 , the method can include: determining functionalproperty feature values for a target S200 and determining variablevalues for a prototype S500.

The method can function to determine the ingredients and/or processparameters that will substantially mimic the functional properties of atarget (e.g., target food). In variants, the method can function todetermine how similar a prototype is to a target, iteratively develop aprototype based on the proximity of the prototype’s functional propertyfeature values to a target (e.g., target functional property featurevalues), select features indicative of a given functional property(e.g., an objective or subjective measure), produce models capable ofdetermining (e.g., predicting) a prototype’s functional property featurevalues and/or otherwise characterizing a prototype, produce modelscapable of determining (e.g., predicting) how a prototype should be made(e.g., manufacturing variable values), and/or provide otherfunctionalities.

2. Examples

In a first example, the method can include: measuring functionalproperty signals for a prototype, extracting functional property featurevalues (e.g., signal feature values; non-semantic feature values) forthe prototype from the prototype functional property signals; measuringfunctional property signals for a target, extracting functional propertyfeature values for the target from the target functional propertysignals; and comparing the extracted prototype functional propertyfeature values to the target functional property feature values (e.g.,using a distance metric, using a clustering method, etc.). Thecomparison can be used to: select which prototype most closely mimicsthe target, determine how to adjust manufacturing variables for the nextprototyping iteration, determine which manufacturing variables are mostpredictive of functional property differences, and/or be otherwise used.

In a second example, the method can include: using a trained model,predicting manufacturing variable values (e.g., ingredients, treatmentspecifications, etc.) that will produce a prototype with a targetcharacterization (e.g., target functional property feature values, atarget classification determined based on functional property featurevalues, etc.). The model can be trained using a set of characterizedsamples, wherein each sample is associated with variable values and asample characterization value (e.g., functional property feature valuesextracted from a measured functional property signal, a classificationdetermined based on functional property feature values, etc.). In anillustrative example, the method can determine a set of functionalproperty feature values (e.g., signal features, non-semantic features,etc.) from measurements of each of a set of potential ingredients, thathave optionally been treated using a set of process parameter values;and determining (e.g., predicting, inferring) which of the potentialingredients, process parameters, and/or quantities or values thereofwould result in a similar analog to a target product, based on thefunctional property feature values. In a specific example, this caninclude predicting a combination of ingredients and/or processparameters that would have functional property feature values similar toa target’s functional property feature values.

However, the method can be otherwise performed.

3. Technical Advantages

Variants of the technology can confer one or more advantages overconventional technologies.

First, evaluating substance analogs (e.g., food replicates) isdifficult. The conventional method of sensory panels is impractical andinaccurate (because the results can be subjective), difficult tonormalize, and noisy. Furthermore, sensory panels are practicallyinefficient to run, especially when testing a large number of foodprototypes. Computational comparison of traditional material sciencefeatures (e.g., food science features, functional property values) isalso insufficient, because traditional (semantic) material sciencefeatures are too coarse to adequately capture the nuances betweendifferent functional properties. For example, mouthfeel and texture canboth be measured using rheology, but are experienced by a human in verydifferent ways. The inventors have discovered that, surprisingly,comparing feature values (e.g., signal feature values, including valuesfor non-semantic and/or semantic features) extracted from the rawfunctional property measurement signals – in addition to or in lieu ofthe conventional semantic features extracted from said signals – is amore accurate representation of the subjective (sensory) adjacency of aprototype to a target food.

Second, complex interactions between manufacturing variable values(e.g., specifying ingredients, manufacturing treatments, other samplespecifications, etc.) can make predicting functional properties of asample challenging. Variants of the technology can train a model topredict a set of manufacturing variable values that will produce aprototype with a target characterization (e.g., target functionalproperty values, target classification, etc.). For example, variants ofthe technology can include training a model using feature valuesextracted from functional property signals, which can increaseprediction accuracy and/or enable a smaller training dataset.

Third, variants of the technology can select a subset of functionalproperty features which are predictive of a sample characterization,which can increase the accuracy and/or precision of the resultantprediction. For example, the subset of functional property features canbe more representative of the subjective (sensory) characterization ofthe sample. In a first specific example, for each of a set of samples,functional property feature values are extracted from a measurementsignal for the sample, wherein each sample is associated with a sampleclassification (e.g., dairy vs non-dairy). The functional propertyfeatures can then be correlated with the sample classifications, andpredictive functional property features can be selected for subsequentanalysis based on the correlation. In a second specific example, liftanalysis can be used (e.g., during and/or after training acharacterization model) to select a subset of functional propertyfeatures with high lift. This feature selection can reduce computationalcomplexity and/or enable human-interpretable annotation of the features.

However, further advantages can be provided by the system and methoddisclosed herein.

4. System

The method can be used with one or more samples. The sample can be aproduct (e.g., food product) and/or be used to manufacture a product(e.g., an intermediate product). In examples, the sample can be a foodproduct, material (e.g., leather, cloth, steel, etc.), gel, headspace,solution, mixture, component(s), ingredient(s), intermediate product,end-stage product, byproduct, and/or any other substance. The sample canbe solid, liquid, gas, a combination thereof, and/or be any other stateof matter.

In variants, samples can include prototypes, targets, and/or othersubstances.

A prototype is preferably a test sample that is intended to mimic atarget (e.g., partially or in its entirety), but can alternatively beany other sample. For example, the prototype can be intended to have oneor more characteristics (e.g., functional property signals, functionalproperty values, functional property feature values, othercharacterization values, etc.) substantially similar to (e.g., within apredetermined margin of error, such as 1%, 5%, 10%, 20%, 30%, etc.) thetarget. For example, the prototype can be: a food analog or replicate(e.g., plant-based dairy analog), a material analog (e.g., plant-basedleather), and/or any other substance. However, the prototype can beotherwise defined.

A target can function as an objective (e.g., gold standard, goal, etc.)for prototype evaluation. The target is preferably a target substance,but can additionally or alternatively be target functional propertyvalues, target functional property signals, target functional propertyfeature values, other target characterization values (e.g.,classifications, cluster locations, cluster identifiers, comparisonmetrics, etc.), a threshold thereof, a range thereof, and/or any othertarget. A target can be a positive target (e.g., where all or parts ofthe method can identify a prototype that is similar to the target)and/or negative target (e.g., where all or parts of the method canidentify a prototype that is dissimilar to the target). However, thetarget can be otherwise defined.

In an example, a prototype can be: a replacement (e.g., analog) for atarget food product (e.g., the prototype can be a plant-based analog foran animal food product), used to manufacture a target food product, afood product with one or more target characterization values, and/or anyother food product. The prototype can be a vegan product, a food productwithout animal products and/or with less animal products (e.g., relativeto a target animal product), a plant-based food product, amicrobial-based food product, a nonmammalian-based food product, and/orany other food product. Examples of target food products include: dairylipids (e.g., ghee, other bovine milk fats, etc.), milk, curds, cheese(e.g., hard cheese, soft cheese, semi-hard cheese, semi-soft cheese),butter, yogurt, cream cheese, dried milk powder, cream, whipped cream,ice cream, coffee cream, other dairy products, egg products (e.g.,scrambled eggs), additive ingredients, mammalian meat products (e.g.,ground meat, steaks, chops, bones, deli meats, sausages, etc.), fishmeat products (e.g., fish steaks, filets, etc.), any animal product,and/or any other suitable food product. In specific examples, the targetfood product includes mozzarella, burrata, feta, brie, ricotta,camembert, chevre, cottage cheese, cheddar, parmigiano, pecorino,gruyere, edam, gouda, jarlsberg, and/or any other cheese. In a specificexample, the prototype includes a milk analog (e.g., a functional milkanalog for cow milk, sheep milk, goat milk, human milk, etc.).

The prototype is preferably entirely plant matter, but can additionallyor alternatively be primarily plant matter (e.g., more than 50%, 60%,70%, 80%, 90%, 95%, 98%, 99%, etc.), partially plant matter, and/or haveany other suitable plant matter content. The prototype can optionallyexclude and/or include less than a threshold amount of total and/oradded: animal products (e.g., excludes animal proteins, such ascaseins), gums (e.g., polysaccharide thickeners), allergenic ingredients(e.g., soy, peanut, wheat, etc.), and/or any other suitable ingredient.Added ingredients and/or compounds can include: materials that were notpresent in and/or are foreign to a plant substrate or other ingredients,materials added in as a separate ingredient, and/or otherwise othercomponents. The threshold amount can be between 0.1%-50% or any range orvalue therebetween (e.g., 40%, 30%, 10%, 5%, 3%, 2%, 1%, 0.1%, etc.),but can alternatively be greater than 10% or less than 0.1%.

However, Samples Can Be Otherwise Defined

Each sample can optionally be associated with values (and/or rangesthereof) for: variables (e.g., to define the sample itself, the samplepreparation process, etc.), functional properties, functional propertyfeatures, other sample characterizations (e.g., classifications,clusters, etc.), and/or any other sample information. The values can bepredetermined (e.g., predetermined variable values, a predeterminedsample class such as ‘dairy’, etc.), predicted, measured, extracted frommeasurements, determined using a model, and/or otherwise determined. Thesystem can optionally include a database, wherein sample informationand/or associated values can be stored in the database or be otherwisestored.

Sample variable values (e.g., process parameters, manufacturing variablevalues, etc.) are preferably specifications prescribing themanufacturing of a sample, but can additionally or alternatively includea sample identifier and/or be otherwise defined. Variable values candefine: manufacturing specifications; the amounts thereof (e.g., ratios,volume, concentration, mass, etc.); temporal parameters thereof (e.g.,when the input should be applied, duration of input application, etc.);and/or any other suitable manufacturing parameter. Manufacturingspecifications can include: ingredients, treatments, and/or any othersample manufacturing input, wherein each specification can be a variableand the variable values can include a value for each specification.Examples of treatments can include: adjusting temperature, adjustingsalt level, adjusting pH level, diluting, pressurizing, depressurizing,humidifying, dehumidifying, agitating, resting, adding ingredients,removing components (e.g., filtering, draining, centrifugation, etc.),adjusting oxygen level, brining, comminuting, fermenting, mixing (e.g.,homogenizing), gelling (e.g., curdling), and/or other treatments.Examples of variable values for treatments can include: treatment type,treatment duration, treatment rate (e.g., flow rate, agitation rate,cooling rate, rotor stator rpm, etc.), treatment temperature, time(e.g., when a treatment is applied, when the sample is characterized,etc.), and/or any other parameters.

Examples of ingredients can include: plant matter, proteins (e.g.,protein isolates), a lipid component (e.g., fats, oils, etc.), anaqueous component (e.g., water, a sucrose solution, etc.),preservatives, acids and/or bases, macronutrients (e.g., protein, fat,starch, sugar, etc.), nutrients, micronutrients, carbohydrates (e.g.,sugars, starches, fibers, polysaccharides, such as maltodextrin, gums,etc.), vitamins, enzymes (e.g., transglutaminase, chymosin, tyrosinase,bromelain, papain, ficain, other cysteine endopeptidases, rennet enzymesand/or rennet-type enzymes, etc.), emulsifiers (e.g., lecithin),particulates, hydrocolloids (e.g., thickening agents, gelling agents,emulsifying agents, stabilizers, etc,; such as starch, gelatin, pectin,and gums, such as agar, alginic acid, sodium alginate, guar gum, locustbean gum, beta- glucan, xanthan gum, etc.), salts (e.g., NaCl, CaCl₂,NaOH, KCl, NaI, MgCl₂, etc.), minerals (e.g., calcium), chemicalcrosslinkers (e.g., transglutaminase) and/or non-crosslinkers (e.g.,L-cysteine), coloring, flavoring compounds, vinegar (e.g., whitevinegar), mold powders, microbial cultures, carbon sources (e.g., tosupplement fermentation), calcium citrate, any combination thereof,and/or any other ingredient. The ingredients can optionally excludeand/or include less than a threshold amount (e.g., 10%, 5%, 3%, 3%, 1%,0.5%, 0.1%, etc.) of added: animal products, animal-derived ingredients,gums (e.g., polysaccharide thickeners), hydrocolloids, allergens,phospholipids, and/or any other suitable ingredient. The ingredients arepreferably food-safe, but can alternatively be not food-safe. Theingredients can be whole ingredients (e.g., include processed plantmaterial), ingredients derived from plant-based sources, ingredientsderived from plant genes, synthetic ingredients, and/or be any otheringredient.

Variable values (e.g., as an output of a variable value model) canoptionally be constrained. Constraints can include binary constraints,thresholds, ranges, ratios, values, and/or any other constraints. In afirst example, the constraints can include predetermined compositionconstraints of the resulting manufactured sample (e.g., a macronutrientconstraint). In an illustrative example, the variable values areconstrained to produce a sample with greater than a thresholdconcentration of protein. In a second example, constraints can bedetermined based on one or more metabolic models. For example, theconstraint can ensure a microbe within the sample can perform aspecified metabolic pathway (e.g., a binary constraint ensuring relevantreagents are present, a threshold constraint ensuring relevant reagentsare present above a threshold concentration, etc.). In a third example,constraints can be learned (e.g., a learned constraint boundary). Forexample, the constraints can be learned during variable value modeltraining, wherein the constraints ensure prototypes meet a target (e.g.,a sample characterization target threshold, a binary samplecharacterization target, etc.). In an illustrative example, constraintsare learned such that variable values that satisfy the constraintsresult in a shreddable prototype. However, the variable values can beotherwise constrained or unconstrainted.

However, sample variable values can be otherwise defined.

Functional properties can optionally function to characterize how thesample behaves during preparation and cooking (e.g., in the case of anintermediate sample used to create a food product) and/or tocharacterize the sample as an end-stage food product (e.g., in look,feel, taste, smell, etc.). Functional properties preferably representbehaviors at the macro-scale, but can additionally or alternativelyrepresent behaviors at micro-scale, nano-scale, and/or any other scale.Functional properties (e.g., characteristics) can include: nutritionalprofile (e.g., macronutrient profile, micronutrient profile, etc.),nutritional quality (e.g., PD-CAAS score), amino acid score, proteindigestibility (PD) score, texture (e.g., texture profile, firmness,toughness, puncture, stretch, compression response, mouthfeel,viscosity, graininess, relaxation, stickiness, chalkiness, flouriness,astringency, crumbliness, stickiness, stretchiness, tearresistance/strength, mouth melt, etc.), solubility, melt profile, smokeprofile, gelation point, flavor, appearance (e.g., color), aroma,precipitation, stability (e.g., room temperature stability), emulsionstability, ion binding capacity, heat capacity, solid fat content,chemical properties (e.g., pH, affinity, surface charge, isoelectricpoint, hydrophobicity/hydrophilicity, chain lengths, chemicalcomposition, nitrogen levels, chirality, stereospecific position, etc.),physiochemical properties, compound concentration (e.g., in the solidsample fraction, vial headspace, olfactory bulb, post-gustation, etc.),denaturation point, denaturation behavior, aggregation point,aggregation behavior (e.g., micellization capability, micelle stability,etc.), particle size, structure (e.g., microstructure, macrostructure,fat crystalline structure, etc.), folding state, folding kinetics,interactions with other molecules (e.g., dextrinization, caramelization,coagulation, shortening, interactions between lipid and protein,interactions with water, aggregation, micellization, etc.), lipidleakage, water holding and/or binding capacity, lipid holding and/orbinding capacity, fatty acid composition (e.g., percentsaturated/unsaturated lipids), moisture level, turbidity, propertiesdetermined using an assay tool, and/or any other properties. However,functional properties can be otherwise defined.

A functional property signal is preferably a data series (e.g., datatime series) associated with a functional property, but can additionallyor alternatively be an array of values (e.g., an array of functionalproperty values), an image, a video, and/or any other set of values. Thefunctional property signal can be 1D, 2D, 3D, and/or any other number ofdimensions. The functional property signal can optionally be a processedsignal (e.g., an aggregate of signals, a smoothed signal, a transformedsignal, etc.). Functional property signals can be experimentallymeasured using an assay tool, determined based on other functionalproperty signals, predicted, and/or otherwise determined. In specificexamples, functional property signals can include measurements for:texture, melt, flavor, and/or any other functional property.

A functional property value is preferably a quantitative valueassociated with a functional property (e.g., viscosity, hardness, timeto break, distance to failure, normalized work, diameter and/or area ofmelt spread, etc.), but can alternatively be a qualitative value (e.g.,‘creamy’) and/or otherwise defined. Functional property values can beobjective (e.g., measured using an assay tool), subjective (e.g.,determined using a human subject), and/or otherwise defined. Functionalproperty values can be discrete, continuous, relative, a classification,numeric, binary, and/or be otherwise characterized. In a specificexample, functional property values can be a binary characteristic(e.g., shreddable or non-shreddable). Functional property values can becalculated from the functional property signals, directly measured usingan assay tool (e.g., without measuring a functional property signal),determined using one or more human subjects, predicted, and/or otherwisedetermined.

Functional property features can function to characterize measurementsignals (e.g., functional property signals) and/or otherwisecharacterize the sample. The functional property features are preferablynon-semantic features (e.g., non-human-interpretable; lacks a physicalanalog or physical interpretation), but can additionally oralternatively be semantic features. Functional property features can beparametric features, non-parametric features, and/or otherwisecharacterized. Functional property features can be features orcharacteristics of: a signal (e.g., a signal feature, the rawmeasurement signal itself, etc.), a time-series (e.g., spectralanalyses, autocovariance, autocorrelation, statistical analyses, etc.),an image (e.g., edge, edgel, corner, blob, area, etc.), one or moremeasurements at a single time-point, functional property values (e.g.,features extracted from functional property values, a functionalproperty value itself, etc.), manufacturing information (e.g., featuresassociated with the cost to manufacture the sample, carbon footprintand/or other sustainability measures of manufacturing, etc.), and/or befeatures or characteristics of any other sample information. However,functional property features can be otherwise defined.

Functional property feature values for a sample are preferably extractedfrom one or more functional property signals (e.g., directly calculatedfrom the signal, based on signal analysis, etc.), but can additionallyor alternatively be predicted (e.g., based on the sample variablevalues), directly determined using an assay, manually determined,randomly determined, and/or otherwise determined. In examples,functional property features values can be or include: values extractedfrom a functional property signal, values extracted from one or moremeasurements at a single time-point, the raw measurement signal,functional property values, values associated with the manufacturing(e.g., cost to manufacture the sample, carbon footprint and/or othersustainability measures of manufacturing, etc.), and/or be any othervalue associated with the sample. An example is shown in FIG. 2 . In aspecific example, functional property feature values are signal featurevalues that are extracted (e.g., using a feature extraction model) fromtime-series measurements taken during an experimental assay (e.g.,rheology measurement) of a sample.

Variable values, functional property values, functional propertysignals, and/or functional property feature values can optionallyinclude an uncertainty parameter (e.g., measurement uncertainty,determined using statistical analysis, etc.).

The system can optionally leverage one or more assays and/or assay toolsto measure: functional property signals, functional property values,variable values, and/or any other sample information. Examples of assaysand/or assay tools that can be used include: a differential scanningcalorimeter (e.g., to determine properties related to melt, gelationpoint, denaturation point, etc.), Schreiber Test, an oven (e.g., for theSchreiber Test), a water bath, a texture analyzer (e.g., puncture test,compression test, extensibility assay, etc.), a rheometer,spectrophotometer (e.g., determine properties related to color),centrifuge (e.g., to determine properties related to water bindingcapacity), moisture analyzer (e.g., to determine properties related towater availability), light microscope (e.g., to determine propertiesrelated to microstructure), atomic force microscope (e.g., to determineproperties related to microstructure), confocal microscope (e.g., todetermine protein association with lipid/water), laser diffractionparticle size analyzer (e.g., to determine properties related toemulsion stability), polyacrylamide gel electrophoresis system (e.g., todetermine properties related to protein composition), mass spectrometry(MS), time-of-flight mass spectrometry (TOF-MS), gas chromatography (GC)(e.g., gas chromatography-olfactometry, GC-MS, etc.; to determineproperties related to aroma/flavor, to determine properties related toprotein composition, etc.), selected ion flow tube mass spectrometry(SIFT-MS), liquid chromatography (LC), LC-MS, fast protein LC (e.g., todetermine properties related to protein composition), proteinconcentration assay systems, thermal gravimetric analysis system,thermal shift (e.g., to determine protein denaturation and/oraggregation behavior), ion chromatography, dynamic light scatteringsystem (e.g., to determine properties related to particle size, todetermine protein aggregation, etc.), Zetasizer (e.g., to determineproperties related to surface charge), protein concentration assays(e.g., Q-bit, Bradford, Biuret, Lecco, etc.), particle size analyzer,sensory panels (e.g., human panelists to determine properties related totexture, flavor, appearance, aroma, etc.), capillary electrophoresis SDS(e.g., to determine protein concentration), spectroscopy (e.g.,fluorescence spectroscopy, circular dichroism, etc.; to determinefolding state, folding kinetics, denaturation temperature, etc.),absorbance spectroscopy (e.g., to determine protein hydrophobicity),CE-IEF (e.g., to determine protein isoelectric point/charge), totalprotein quantification, high temperature gelation, microbial cloning,Turbiscan, stereospecific analysis, olfactometers, electrophysiologicaltesting (e.g., of a human olfactometer), psychophysical testing (e.g.,of a human olfactometer), and/or any other assay and/or assay tool.

The system can include one or more models, including feature extractionmodels, feature selection models, characterization models, variablevalue models, correlation models, metabolic models, and/or any othermodel. The models can include or leverage: regression (e.g., leverageregression, etc.), classification, neural networks (e.g., CNN, DNN, CAN,LSTM, RNN, autoencoders, etc.), rules, heuristics, equations (e.g.,weighted equations, etc.), selection (e.g., from a library),instance-based methods (e.g., nearest neighbor), regularization methods(e.g., ridge regression), decision trees, Bayesian methods (e.g., NaiveBayes, Markov, etc.), Markov methods (e.g., hidden Markov models),kernel methods, deterministics, genetic programs, encoders, supportvectors, association rules, ensemble methods, optimization methods,statistical methods (e.g., probability), comparison methods (e.g.,matching, distance metrics, thresholds, etc.), dimensionality reduction(e.g., principal component analysis, t-distributed stochastic neighborembedding, linear discriminant analysis, etc.), clustering methods(e.g., k-means clustering, hierarchical clustering, etc.), any machinelearning model, and/or any other suitable method or model.

Models can be trained, learned, fit, predetermined, and/or can beotherwise determined. Models can be trained using self-supervisedlearning, semi-supervised learning, supervised learning, unsupervisedlearning, transfer learning, reinforcement learning, and/or any othersuitable training method.

The feature extraction model can function to extract values forfunctional property features for a sample. The feature extraction modelcan output functional property feature values based on functionalproperty signals (e.g., from one or more assays), functional propertyvalues, other measurements, variable values, and/or any other data. Thefeature extraction model can use: time series analysis, decompositiontechniques (e.g., time series decomposition), sequence comparisontechniques (e.g., dynamic time warping), subsequence discoverytechniques, peak detection, signal transforms (e.g., Fourier transform,fast Fourier transform, Laplace transform, etc.), regression, imageprocessing techniques, classifiers, Markov models, statistical methods,encoders (e.g., trained to encode functional property signals and/orvariable values to a shared latent space), and/or any other featureextraction methods. The feature extraction model can optionally producea processed functional property signal (e.g., a decomposed signal, atransformed signal, etc.), wherein the feature values are extractedbased on the processed signal. An example is shown in FIG. 2 .

The feature extraction model can be a single model, an ensemble model,and/or any other arrangement of one or more models. In a first example,the feature extraction model includes a first model that outputsnon-semantic feature values based on one or more functional propertysignals and/or functional property values for a sample, and a secondmodel that outputs semantic feature values based on one or morefunctional property values and/or functional property signals (e.g., thesame functional property signals and/or different functional propertysignals). In a third example, the feature extraction model is a singlemodel that outputs non-semantic feature values (e.g., only non-semanticfeature values) based on one or more functional property signals,functional property values, and/or other data. In a fourth example, thefeature extraction model is a single model that outputs a combination ofsemantic and non-semantic feature values based on one or more functionalproperty signals, functional property values, and/or other data.

In a first embodiment, the feature extraction model can include and/orbe based on predetermined model. In a first illustrative example, thefeature extraction model can include a predetermined time seriesdecomposition model, wherein the feature extraction model can extractfeature values by decomposing a functional property signal into one ormore components and determining the feature values based on the one ormore components. In a second illustrative example, the featureextraction model can include a peak detection model, wherein the featureextraction model can extract distance to failure by determining a peakof a force versus extension signal. In a second embodiment, the featureextraction model can be trained on a predetermined training value forthe feature (e.g., using supervised learning). In a third embodiment,the feature extraction model can be a subset of the layers from a modeltrained end-to-end to predict another attribute (e.g., wherein thefunctional property features can be learned features). In a firstillustrative example, the feature extraction model can be a subset oflayers (e.g., the first several layers, feature extraction layers,intermediary layers, etc.) of a variable value model trained to predictvariable values for a second prototype from: variable values, functionalproperty signals, functional property values, and/or other inputs for afirst prototype. In a second illustrative example, the featureextraction model can be a subset of layers of a characterization modeltrained to predict a sample characterization value from variable values,functional property signals, functional property values, and/or otherinputs for the sample.

However, the feature extraction model can be otherwise configured.

The optional characterization model can function to determine (e.g.,predict) one or more characterization values for a sample. A samplecharacterization value can include: a functional property signal;functional property value; functional property feature value;classification; cluster location; cluster identifier; comparison metricvalue between two samples (e.g., a prototype and a target); and/or anyother parameter characterizing the sample. In variants, thecharacterization model can generate synthetic measurements and/orfeature values for a hypothetical sample. In a first example, the samplecharacterization value is a vector, wherein each vector position canrepresent a different functional property feature and the vector valuecan represent the predicted value for said functional property feature.In a second example, the sample characterization value is a vector,wherein each vector position can represent a different functionalproperty and the vector value can represent the predicted value for saidfunctional property. In a third example, the sample characterizationvalue is a classification. In a first illustrative example, theclassifications include ‘dairy’ and ‘non-dairy’. In a secondillustrative example, the classifications include ‘blue cheese’, ‘feta’,‘plant-based blue cheese’, and ‘plant-based feta.’ In a fourth example,the sample characterization value is a cluster identifier (e.g., ‘bluecheese’, ‘dairy’, ‘plant-based cheese’, etc.). In a fifth example, thesample characterization value is a cluster location (e.g., coordinatesin feature space) representing: a point within a cluster, the clustercentroid, the cluster boundary, and/or any other location associatedwith one or more clusters. In a sixth example, the samplecharacterization is a comparison metric (e.g., as described in S400).

The characterization model inputs can include: variable values for asample, functional property feature values for a sample, correlationinformation (e.g., outputs from the correlation model), and/or any othersample information. The characterization model outputs can include oneor more sample characterization values and/or any other sampleinformation. In a first variant, the characterization model outputs oneor more sample characterization values based on functional propertyfeature values associated with the sample (e.g., functional propertyfeature values extracted from a measured functional property signal,functional property feature values predicted based on sample variablevalues using a different characterization model, etc.). In a secondvariant, the characterization model outputs one or more samplecharacterization values based on variable values associated with thesample. The characterization model can be trained via S650 and/orotherwise trained.

The characterization model can optionally interface with and/or be partof: a correlation model, feature selection model, and/or any other model(e.g., DNN, etc.). The characterization model can include a single modeland/or multiple models. When the characterization model includesmultiple models, the models can be arranged in series, in parallel, asdistinct models, and/or otherwise arranged. When the characterizationmodel includes multiple models, the models can be trained separately(e.g., using distinct training data sets), trained together (e.g., usingthe same training data set, using different subsets of the same trainingdata set, etc.), and/or otherwise trained.

However, the Characterization Model Can Be Otherwise Configured

The optional variable value model can function to determine (e.g.,predict) variable values (e.g., for a second prototype) that wouldproduce a prototype with a target characterization value and/or make aprototype more or less similar to a target.

The variable value model inputs can include: variable values for asample (e.g., a first prototype), sample characterization values (e.g.,comparison metric for the first prototype), a target (e.g., targetsample characterization values), correlation information (e.g., outputsfrom the correlation model), and/or any other sample information. Thevariable value model outputs can include variable values for a sample(e.g., a second prototype), variable value adjustments (e.g., relativeto variable values for a first prototype), and/or any other sampleinformation. In a first variant, the variable value model outputsvariable value adjustments for a second prototype based on samplecharacterization values (e.g., a comparison metric) for a firstprototype. In a second variant, the variable value model outputsvariable values for a prototype in a set of prototypes based on samplecharacterization values (e.g., the functional property feature values,the comparison metrics, etc.) for each of the set of prototypes. Forexample, a comparison metric can be determined for each of the set ofprototypes (e.g., based on measured functional property signals, using acharacterization model, using a first set of layers of the variablevalue model, etc.), wherein the top-ranked prototype and associatedvariable values (e.g., with the lowest comparison metric, highestcomparison metric, etc.) is selected by the variable value model. In athird variant, the variable value model outputs variable values for aprototype based on a target characterization (e.g., associated with atarget sample). For example, the target characterization can includetarget functional property values, target functional property featurevalues, a target classification, a target comparison metric, and/or anyother target. In an illustrative example, the variable value modeloutputs variable values (e.g., binary include / exclude; amounts; etc.)for each of a set of candidate ingredients for the next prototype basedon the functional property feature values for each ingredient andoptionally the functional property feature values for the target. Thevariable value model can be trained via S600 and/or otherwise trained.

The variable value model can optionally interface with and/or be partof: a correlation model, feature selection model, characterizationmodel, metabolic model (e.g., to ensure variable values satisfy one ormore constraints), and/or any other model (e.g., DNN, etc.). Thevariable value model can include a single model and/or multiple models.When the variable value model includes multiple models, the models canbe arranged in series, in parallel, as distinct models, and/or otherwisearranged. In an example, a characterization model and a variable valuemodel can be used in series (e.g., wherein the characterization modeldetermines a sample characterization for a first prototype and avariable model determines variable values for a second prototype basedon the first prototype sample characterization). When the variable valuemodel includes multiple models, the models can be trained separately(e.g., using distinct training data sets), trained together (e.g., usingthe same training data set, using different subsets of the same trainingdata set, etc.), and/or otherwise trained.

However, the variable value model can be otherwise configured.

The optional correlation model can function to determine thecorrelation, interaction, and/or any other association between samplevariables and sample characterizations (e.g., functional propertyfeatures, classifications, clusters, etc.) and/or between functionalproperty features and sample characterizations. The correlation modelcan use: classifiers, support vectors, artificial neural networks(ANNs), random fields (e.g., Markov random field, conditional randomfield, etc.), K-nearest neighbors, statistical methods, regression(e.g., coefficients, etc.), black box methods (e.g., LIME, SHAP values,contrastive explanations deconvolution, etc.), and/or any other method.

In a first variant, the correlation model inputs can include variablevalues, sample characterization values (e.g., functional propertyvalues, classifications, cluster identifiers, cluster locations,comparison metrics, etc.; wherein the variable values andcharacterizations are associated via common samples), the systemdatabase, and/or any other information. The correlation model outputscan include a mapping between variables (e.g., variables, variablevalues, ranges of values, etc.) and sample characterizations (e.g.,functional properties, functional property values, functional propertyfeatures, functional property feature values, classifications, clusteridentifiers, cluster locations, comparison metrics, ranges of values,etc.). The mapping can include: correlation coefficients (e.g., negativeand/or positive), interaction effects (e.g., negative and/or positive,where a positive interaction effect can represent an increasedsignificance effect of variable A on a sample characterization when inthe presence of variable B), an association, confidence scores thereof,and/or other correlation metric. An example is shown in FIG. 6 .

In a second variant, the correlation model inputs can include functionalproperty feature values, sample characterization values (e.g.,functional property values, classifications, cluster identifiers,cluster locations, comparison metrics, etc.; wherein the functionalproperty feature values and characterizations are associated via commonsamples), the system database, and/or any other information. Thecorrelation model outputs can include a mapping between functionalproperty features (e.g., features, feature values, ranges of values,etc.) and sample characterizations (e.g., classifications, clusteridentifiers, cluster locations, comparison metrics, ranges of values,etc.). The mapping can include: correlation coefficients, interactioneffects (e.g., negative and/or positive, where a positive interactioneffect can represent an increased significance effect of feature A on asample characterization when in the presence of feature B), anassociation, confidence scores thereof, and/or other correlation metric.

The correlation model can optionally be trained on a set ofcharacterized samples (e.g., characterized with: functional propertysignals, functional property feature values, functional property values,a classification, a cluster identifier, a cluster location, comparisonmetric, other characterization values, etc.). In a first variant, thecorrelation model can identify similar and/or divergent variable values(e.g., calculating an implicit and/or explicit similarity measure)between samples and correlate those variables to samplecharacterizations. For example, variables with differing values (e.g.,across samples) can be mapped to the characterizations with differingvalues (e.g., across the same samples). In a second variant, thecorrelation model can identify similar and/or divergent functionalproperty feature values between samples and correlate those functionalproperty features to sample characterizations. For example, functionalproperty features with differing values (e.g., across samples) can bemapped to the characterizations with differing values (e.g., across thesame samples).

However, the correlation model can be otherwise configured.

The optional feature selection model can function to reduce featuredimensions, to select and/or weight features likely influencing (e.g.,predictive of) sample characterizations, and/or to select and/or weightfeatures representative of a human sensory evaluation. For example, thefeature selection model can function to select a feature subsetincluding features (e.g., predictors) that are relevant to: comparisonsbetween a prototype and a target, one or more functional properties(e.g., hardness, creaminess, etc.), other sample characterizations,and/or other uses.

The feature selection model inputs can include: functional propertyfeatures, functional property feature values, other samplecharacterizations (e.g., classifications, clusters, etc.),characterization values, correlation information (e.g., outputs from thecorrelation model, correlation coefficients, interaction effects, etc.),the system database, and/or any other sample information. The featureselection model outputs can include: a functional property featuresubset, functional property feature weights (e.g., wherein features inthe subset are weighted based on feature importance), and/or any othersuitable outputs. The feature selection model can use: supervisedselection (e.g., wrapper, filter, intrinsic, etc.), unsupervisedselection, embedded methods, recursive feature selection, lift analysis(e.g., based on a feature’s lift), any explainability and/orinterpretability method (e.g., SHAP values), and/or any other selectionmethod. The feature selection model can be a correlation model (and/orvice versa), can include a correlation model (and/or vice versa), cantake correlation model outputs as inputs (and/or vice versa), beotherwise related to a correlation model, and/or be unrelated to acorrelation model.

The feature selection model can optionally be trained to select relevantfunctional property features for sample characterization valuedetermination and/or variable value determination. For example, thetraining target can be a subset of functional property features withhigh (positive and/or negative) interaction effects and/or correlationwith sample characterizations (e.g., a correlation coefficient for afunctional property feature given a target characterization value,interaction coefficients for functional property features, whether anexpected correlation and/or interaction was validated and/orinvalidated, etc.). However, the feature selection model can beotherwise trained.

However, the feature selection model can be otherwise configured.

The optional metabolic model can function to prescribe metabolicprocesses that can occur during the sample manufacturing process. Themetabolic model can: specify variable values; dictate constraints forvariable values (e.g., values for end-stage variables, ingredients thatwill result in target end-stage variables, etc.) and/or samplecharacterization values (e.g., predicted and/or otherwise determined);determine available metabolic pathways (e.g., based on sample variablevalues); and/or be otherwise used. Metabolic models can be implementedusing domain knowledge of the metabolic pathways and/or be otherwiseimplemented. However, the metabolic model can be otherwise configured

However, models can be otherwise defined.

All or part of the system and/or method can be implemented using acomputing system. The computing system can include one or more: CPUs,GPUs, custom FPGA/ASICS, microprocessors, servers, cloud computing,and/or any other suitable components. The computing system can be local,remote (e.g., cloud), distributed, or otherwise arranged relative to anyother system or module.

5. Method

As shown in FIG. 1 , the method can include: determining functionalproperty feature values for a target S200, and determining variablevalues for a prototype S500. The method can optionally include:determining functional property feature values for a first prototypeS100, processing functional property features S300, comparing the firstprototype functional property feature values to the target functionalproperty feature values S400, training a variable value model S600,training a characterization model S650, and/or any other suitable steps.

All or portions of the method can be performed once (e.g., for a giventarget), multiple times (e.g., to develop a database of characterizedsamples), iteratively (e.g., to iteratively improve prototypes, to traina model, etc.), concurrently, asynchronously, periodically, in real time(e.g., responsive to a request), and/or at any other suitable time. Allor portions of the method can be performed automatically, manually,semi-automatically, and/or otherwise performed. All or portions of themethod can be performed by one or more components of the system, using acomputing system, using a database (e.g., a system database, athird-party database, etc.), by a user, and/or by any other suitablesystem. The method is preferably performed for a combination offunctional properties of a target for each iteration (e.g., theresultant sample has values for functional properties similar to valuesfor multiple functional properties of the target), but can alternativelybe performed for a single functional property (e.g., the sample hasvalue(s) for a single functional property similar to the target value(s)for the functional property; the sample is iteratively refined on afunctional property-by-functional property basis) and/or for any othersuitable number of functional properties for each iteration.

The method can optionally include determining functional propertyfeature values for a first prototype S100, which functions tocharacterize functional property signals measured for the firstprototype. S100 can be performed after a physical prototype hasundergone one or more experimental assays (e.g., after functionalproperty measurement signals have been determined), without performingexperimental assays, and/or at any other time. S100 can be performedafter the prototype has been manufactured, during manufacturing, withoutmanufacturing a physical prototype (e.g., in the case of predictedfunctional property feature values), and/or at any other time. S100 canbe performed one or more times for each prototype, wherein multiplevalues for a given functional property feature can optionally beaggregated (e.g., averaged, summed, etc.). S100 can optionally berepeated for each prototype in a set of prototypes.

The first prototype’s functional property feature values can beextracted, predicted (e.g., based on the first prototype variablevalues, using a characterization model), predetermined, determinedmanually, determined automatically, determined with a model (e.g., afeature extraction model), retrieved from a database (e.g., whereprototype functional property feature values are those associated withthe prototype in a database), synthetically generated, determinedrandomly, and/or be otherwise determined.

In a first variant, S100 can include measuring one or more functionalproperty signals using an assay and/or assay tool, optionally extractingsignal feature values (e.g., where the functional property featurevalues includes the signal feature values) from the functional propertysignals (e.g., using a feature extraction model), and/or optionallyextracting functional property values from the functional propertysignals (e.g., where the functional property feature values includes thefunctional property values). S100 can optionally include aggregatingmultiple functional property signals (e.g., texture signal, rheologysignal, etc.), and extracting signal features from the aggregatedsignals. S100 can optionally include aggregating functional propertyfeature values (e.g., into a single set of functional property values),wherein the functional property feature values can be determined usingdifferent measurement modalities (e.g., different assays); an example isshown in FIG. 7 . In all or parts of the method, the measurementprotocol (e.g., assay protocol) for different samples (e.g., samplesthat are compared against each other such as prototypes and targets) arepreferably the same, but can alternatively be different.

In a first example, a texture signal (e.g., including force and/orextension/displacement versus time, including force versusextension/displacement, etc.) for a food prototype can be measured usinga texture analyzer. The texture signal can be from: a puncture test, atexture profile analysis (e.g., a compression test), an extensibilityassay, and/or any other texture test or measurement. The featureextraction model can extract a set of signal feature values from thetexture signal (e.g., time series characteristics extracted using timeseries analysis techniques). In an illustrative example, this caninclude: subjecting the prototype to a puncture test, sampling thepuncture test waveforms, and extracting signal features from eachpuncture test waveform. The texture signal can additionally oralternatively be used to determine functional property values (e.g.,values for hardness, spreadability, distance to failure, normalizedwork, etc.).

In a second example, S100 can include: measuring the rheology of aprototype, obtaining the rheology measurement signal (e.g., raw signal),and extracting signal features from the measurement signal.

In a third example, S100 can include: subjecting the prototype to aSchreiber test, sampling a time series of images throughout the test,and extracting image features from at least a subset of the images(e.g., prototype area, radius, distribution of edges, histogram ofgradients, etc.).

In a fourth example, S100 can include: using a GC-MS assay tool tomeasure odorants and/or any molecules in the volatile phase for a sample(e.g., from a gaseous headspace, from human breath after tasting asample, etc.), and directly determining flavor functional propertyfeature values from the measurement values (e.g., where the featurevalues are the measurement values, where the feature values areprocessed measurement values, etc.).

In a fifth example, S100 can include: determining a first set ofnutritional quality functional property feature values (e.g., via aPDCAAS method, retrieving the values from a database, etc.), andoptionally extracting a second set of nutritional quality functionalproperty feature values from amino acid sequences of proteins in thesample.

In a sixth example, S100 can include using one or more human paneliststo evaluate a functional property (e.g., a binary evaluation, asubjective score, etc.), and determining functional property featurevalues based on the panelist evaluation(s). For example, the functionalproperty feature values can be the panelist evaluation(s), be anaggregate of evaluations, be extracted from the evaluation(s), and/or beotherwise determined based on the evaluations.

In a second variant, S100 can include predicting functional propertyfeature values based on variable values for the first prototype usingthe characterization model (e.g., trained via S650).

However, prototype functional property feature values can be otherwisedetermined.

Determining functional property feature values for a target S200functions to specify one or more criteria for prototype evaluation. S200can be performed after a physical target has undergone one or moreexperimental assays (e.g., the same experimental assays as S100),without performing experimental assays, and/or at any other time. S200can be performed after the target has been manufactured, duringmanufacturing, without manufacturing a physical target (e.g., in thecase of predetermined target characterization values), and/or at anyother time. S200 can be performed one or more times for a target,wherein multiple values for a given functional property feature canoptionally be aggregated (e.g., averaged, summed, etc.).

The target’s functional property feature values can be extracted,predicted (e.g., based on the target variable values, using acharacterization model), predetermined, determined manually, determinedautomatically, determined with a model (e.g., a feature extractionmodel), retrieved from a database (e.g., where target functionalproperty feature values are those associated with the target in adatabase), synthetically generated, determined randomly, and/or beotherwise determined.

S200 can be performed using any methods described in S100. S200 ispreferably performed in a similar manner to S100 (e.g., leverages thesame methodologies, using the target instead of the prototype, etc.),but can alternatively be different. The assay types, assay protocols,transformations, feature extraction methods, and extracted functionalproperty features are preferably the same for S200 as in S100, but canadditionally or alternatively be different.

However, target functional property feature values can be otherwisedetermined.

The method can optionally include processing functional propertyfeatures S300, which functions to select and/or weight features likelyinfluencing (e.g., having a measurable effect on, a significant effecton, a disproportionate effect, etc.) sample characterizations, to selectand/or weight features representative of a human sensory evaluation,and/or to reduce feature space dimensions (e.g., to reduce computationalload). S300 can be performed after S100, after S200, after S400, beforeS400, before S500, during S600, after S600, during S650, after S650,and/or at any other time.

Processing functional property features can include weighting thefeatures, selecting a subset of the features, aggregating features,and/or otherwise processing the features. The features can be processedusing a feature selection model, using a correlation model, randomly,with human input, and/or be otherwise processed. In all or parts of themethod, functional property features can optionally be processedfunctional property features (e.g., a subset of functional propertyfeatures, weighted functional property features, aggregated functionalproperty features, etc. etc.), wherein functional property featurevalues are values for the processed functional property features.

In a first variant, the functional property features can be processed(e.g., selected and/or weighted) based on the influence of eachfunctional property feature on sample characterization. In a firstembodiment, the feature selection model uses lift analysis (e.g.,applied to a trained characterization model and/or a trained variablevalue model) to weight features based on their associated lift and/or toselect the subset of features with lift above a threshold. In a secondembodiment, functional property features are processed based on theirassociated weights used for the characterization model and/or variablevalue, wherein the model weights can be determined during and/or aftermodel training. For example, functional property features with modelweights above a threshold value are selected as a functional propertyfeature subset. In a third embodiment, a correlation model can be usedto determine functional property features positively and/or negativelycorrelated to one or more sample characterizations (e.g., absolute valueof correlation coefficient above a threshold, a confidence score above athreshold, etc.).

In a second variant, a subset of functional property features can bedetermined using any dimensionality reduction technique (e.g., principalcomponent analysis, linear discriminant analysis, etc.).

In a third variant, functional property features can be processed (e.g.,selected and/or weighted) based on a comparison between a target and aprototype (e.g., via S400 methods), wherein the functional propertyfeatures can be processed based on the comparison (e.g., wherein theprocessed functional property features can be used for analysis of asecond prototype). For example, a difference between functional propertyfeature values associated with the target and prototype can bedetermined (e.g., where one or more sample characterization valuesdiffer between the two sets). The functional property featuresassociated with the differing functional property feature values candefine the functional property feature subset and/or be given higherweights.

However, the functional property features can be otherwise processed.

The method can optionally include comparing the first prototypefunctional property feature values to the target functional propertyfeature values S400, which functions to determine whether the firstprototype has a desired characterization (e.g., whether the prototype issimilar to or dissimilar from the target). The target functionalproperty feature values can be determined using another iteration ofS100-S300 (e.g., using the target instead of the prototype or sample),but can be otherwise determined. S400 can be performed after S100, afterS200, after S300, and/or any other time. S400 can be performed for eachmeasurement assay, performed for multiple subsets of the functionalproperty feature values (e.g., performed once for flavor feature values,once for texture feature values, etc.), performed for all functionalproperty feature values (e.g., aggregated across feature valuesextracted from measurements acquired via differing assay tools), and/orbe otherwise performed.

Comparing prototype functional property feature values to the targetfunctional property feature values preferably includes determining acomparison metric (e.g., to quantify, classify, and/or otherwise definesimilarity). The comparison metric can be qualitative, quantitative,relative, discrete, continuous, a classification, numeric, binary,and/or be otherwise characterized. The comparison metric can be orinclude: a distance, difference, ratio, regression, residuals,clustering metric (e.g., wherein multiple samples of the first prototypeand/or targets are evaluated, wherein multiple prototypes and/or targetsare evaluated, etc.), statistical measure, rank, classification, vector,functional property features and/or values thereof, any similaritymetric, and/or any other comparison measure. In an example, thecomparison metric is a distance in functional property feature space(e.g., wherein a functional property value set for a sample is anembedding in the feature space). In a specific example, the comparisonmetric is low (e.g., the first prototype is similar to the target) whenthe first prototype functional property feature values are near (infeature space) positive target functional property feature values and/orfar from negative target functional property feature values. Thecomparison metric can optionally include metrics for variance and/or anyother statistical measure (e.g., for a single prototype, for multipleprototypes with similar variable values, for a cluster of functionalproperty feature values, etc.).

The comparison metric can be determined using the characterizationmodel, comparison methods (e.g., matching, distance metrics, etc.),thresholds, regression, selection methods, classification, neuralnetworks (e.g., CNNs, DNNs, etc.), clustering methods, rules,heuristics, equations (e.g., weighted equations, etc.), and/or any othermethods.

In a first variant, the comparison metric can be determined usingclustering analysis. Functional property feature values (e.g., for thefirst prototype, for the target, for other samples, etc.) can beclustered, wherein the comparison metric is determined based on theclusters. The clustering dimensions can be based on individual features,aggregate features, learned dimensions, and/or any other dimensions infeature space. Cluster analyses can include connectivity-basedclustering (e.g., hierarchical clustering), centroid-based clustering,distribution-based clustering, density-based clustering, grid-basedclustering, and/or any other cluster analysis.

In a first embodiment, clustering is supervised (e.g., thecharacterization model is trained using labeled functional propertyvalue sets). In a first example, sets of functional property featurevalues for sample instances (e.g., prototype instances, targetinstances) with the same variable values are clustered together (e.g.,labeled with the same, known cluster identifier). In a second example,sets of functional property feature values for sample instances with thesame sample class (e.g., a predetermined sample class) are clusteredtogether. In a first illustrative example, multiple varieties of bluecheeses can be clustered together (e.g., where the target is the bluecheese class). In a second illustrative example, multiple instances ofthe same specific blue cheese type (within a larger blue cheese class)can be clustered together (e.g., where the target is the specific bluecheese type). In a second embodiment, clustering is unsupervised. Forexample, the characterization model can be trained using sets ofcharacterized samples, each with a functional property value set,wherein clusters of samples are learned.

In this variant, the comparison metric is preferably based on adistance, but can additionally or alternatively be based on astatistical measure (e.g., correlation coefficient, outlier metric,etc.), a cosine similarity, a classification, and/or be otherwisedetermined. In a first example, the comparison metric can be a binaryvalue indicating whether the prototype functional property value set isclassified with a target cluster (e.g., the cluster identifier for theprototype functional property value set is the target cluster identifierassociated with target functional property feature value set(s)). In asecond example, the comparison metric can be a distance between theprototype functional property feature value set (plotted inN-dimensional clustering feature space) and a location associated with atarget cluster (e.g., the centroid, cluster boundary, etc.); an exampleis shown in FIG. 8 . In a third example, the comparison metric can be adistance between a location associated with a prototype cluster (e.g.,the cluster associated with prototype functional property feature valueset(s)) and a location associated with a target cluster; an example isshown in FIG. 9A. In a third example, the comparison metric can be adistance between the prototype functional property feature value set andthe target functional property feature value set (e.g., within the samecluster, across clusters, etc.); an example is shown in FIG. 9B.

In a second variant, the comparison metric can be determined (e.g.,predicted, inferred, etc.) using an algorithm applied to the prototypefunctional property feature values and target functional propertyfeature values. In a first example, the algorithm can include adifference, a ratio, and/or any other comparison algorithm for eachfunctional property feature and/or for aggregated functional propertyfeatures. In a first specific example, the comparison metric is a vectorof differences (e.g., differences, squared differences, etc.) betweenvalues for each functional property feature. In a second specificexample, the comparison metric is a subset of functional propertyfeatures with a difference (e.g., significant difference, differenceabove a threshold, the greatest differences, etc.) between the prototypefunctional property feature value and the target functional propertyfeature value. In a second example, the algorithm can include a vectordistance calculation between a vectorized prototype functional propertyfeature value set and a vectorized target functional property featurevalue set.

In a third variant, the comparison metric can be a classification (e.g.,determined using the characterization model). In a first example, thecomparison metric can classify similarity (e.g., in discrete bins). In asecond example, the comparison metric can classify a prototype as one ofa set of sample classes (e.g., based on the distance between theprototype and each sample in feature space), wherein one or more of thesample classes is a target class. In an illustrative example, thecomparison metric is a binary value indicating whether the prototype wasclassified in the target class.

In any variant, the target can be a positive target, be a negativetarget, and/or include both positive and negative targets. In a firstexample, the comparison metric can be calculated based on whether thecluster location for the prototype functional property feature value setis closer to a positive or negative target cluster. In a second example,the comparison metric can increase when the prototype functionalproperty feature values are more similar to positive targets anddecrease when the prototype functional property feature values are moresimilar to negative targets (or vice versa). In a third example, thecomparison metric can be calculated based on whether the prototypefunctional property feature value set is classified as a positive ornegative target class.

However, prototype functional property values and target functionalproperty values can be otherwise compared.

Determining variable values for a prototype S500 can function todetermine how to adjust variable values for a subsequent prototypingiteration, to determine variable values that will produce a prototypematching a target, and/or to select a prototype that most closely mimicsa target. S500 can be performed after S200, after S400, without S400,after S600 (e.g., using a trained variable value model), after S650,and/or at any other time. S500 can optionally be iteratively performed(e.g., iteratively updating variable values for a successive prototype).After the variable values have been determined for the prototype (e.g.,the second prototype), all or parts of the method can be optionallyrepeated for this prototype (e.g., wherein S100 is performed for thesecond prototype and S500 is repeated to determine variable values for athird prototype).

The variable values can be determined using one or more models (e.g.,the variable value model, characterization model, etc.), rules,heuristics, equations, selection methods, optimization methods, withuser input (e.g., manually determined, determined using domainknowledge, modifying a model prediction, etc.), randomly determined,and/or otherwise determined.

In a first variant, S500 includes selecting a prototype (with associatedvariable values) from a set of candidate prototypes based oncharacterization values determined for each candidate prototype. In anexample, the candidate prototypes-each with different variablevalues-can be ranked based on their associated characterization values(e.g., a comparison metric determined via S400, functional propertyfeature values determined via S100, etc.), wherein the top-rankedprototype is selected (e.g., using the variable value model, using aheuristic, etc.). The top-ranked prototype can be: the prototype that isthe closest overall to a positive target, the overall furthest from anegative target, the prototype with a specific feature value closestand/or furthest from a target, and/or be otherwise defined. An exampleis shown in FIG. 3C.

In a second variant, the variable values can be updated variable valuesfor a second prototype, determined based on the sample characterizationvalues (e.g., comparison metric) and variable values for a firstprototype. Examples are shown in FIG. 3A and FIG. 3B.

In a first embodiment of the second variant, S500 can include predicting(e.g., using the characterization model) an impact of an increase and/ordecrease of a variable value (relative to the first prototype variablevalues) on the sample characterization values, wherein a variable valueresulting in a desired sample characterization value change can beselected for the second prototype. In examples, the desired samplecharacterization value change can be an increase or decrease in thecomparison metric, a change in one or more functional property featurevalues, a change in a sample classification, and/or any other change.

In a second embodiment of the second variant, S500 can includedetermining variable values based on a functional property feature valuegap between the first prototype functional property feature values andthe target functional property feature values (e.g., wherein the gap isthe comparison metric determined via S400). The gap can include apositive and/or negative difference for each functional propertyfeature, a vector distance from prototype functional property featurevalues to target functional property feature values, and/or any othercomparison metric. In a first example, the variable values for thesecond prototype) can then be predicted (e.g., using the variable valuemodel) based on the variable values for the first prototype and thefunctional property feature value gap. In a second example, one or moredeviant functional property features can be identified based on the gap,wherein the values for the deviant functional property features differ(e.g., significantly differ) between the first prototype and the target.The one or more functional properties associated with the deviantfunctional property feature(s) can then be surfaced to a user, whereinthe user selects a new variable value (e.g., based on the surfacedfunctional properties and domain knowledge).

In a third embodiment of the second variant, characterization values forthe first prototype can be determined using the characterization model,wherein explainability and/or interpretability methods can be applied tothe characterization model to determine the updated variable values. Ina first illustrative example, the characterization model outputs a(predicted) non-target classification for the first prototype based onthe first prototype variable values, and explainability methods are usedto identify the variable values that can be adjusted (and how thevariable values can be adjusted) to increase the likelihood that thecharacterization model will output a (predicted) target classificationfor the second prototype. In a second illustrative example, thecharacterization model outputs a (predicted) non-target classificationfor the first prototype based on the first prototype functional propertyfeature values, and explainability methods are used to identify thefunctional property feature values that are highly influential incausing the non-target classification. The updated variable values canthen be determined based on the identified functional property featurevalues (e.g., using variable value model; manually updating variablevalues based on the functional property associated with the identifiedfunctional property feature values; etc.).

In a third variant, the variable values can be (directly) predictedbased on target sample characterization values (e.g., target functionalproperty feature values). An example is shown in FIG. 3D. In an example,the variable values can be predicted such that a prototype manufacturedbased on the predicted variable values will likely result in the targetfunctional property feature values. The variable values are preferablypredicted using the variable value model, but can additionally oralternatively be predicted using the characterization model, thecorrelation model (e.g., with a mapping between variable values andfunctional property feature values determined using the correlationmodel), any other model, domain knowledge, and/or otherwise predicted.

However, variable values for a prototype can be otherwise determined.

Optionally, the method can include training a variable value model S600,which functions to train the variable value model to predict variablevalues. S600 can be performed before S500, after S650, concurrently withS650, asynchronously from S650, after a set of samples have beencharacterized with sample characterization values (e.g., using S100/S200methods, using S400 methods, etc.), iteratively, periodically (e.g., anew samples are characterized), and/or at any other time.

Training the variable value model can include determining training datafor a set of training samples (e.g., characterized samples), wherein thetraining data can include: variable values, sample characterizationvalues (e.g., functional property feature values, classifications,comparison metrics, etc.), and/or any other data for each of the settraining samples. The training data can be predetermined (e.g.,predetermined target characterization values), measured and/ordetermined based on measurements (e.g., using S100/S200 methods),synthetically generated, randomly determined, and/or otherwisedetermined.

In a first embodiment, the variable value model can be trained to outputvariable values for a prototype based on input sample characterizationvalues (e.g., target functional property feature values). In a firstexample, the variable value model can be trained based on feedback froma comparison between predicted variable values (e.g., predicted based onactual sample characterization values for a training sample using thevariable value model) and the actual variable values for the trainingsample. An example is shown in FIG. 5B. In a second example, thevariable value model can be trained based on feedback from a comparisonbetween target characterization values and characterization values(e.g., functional property feature values) determined based onmeasurements for a training sample manufactured using variable valuespredicted using the variable value model (e.g., predicted based on thetarget characterization values). An example is shown in FIG. 5C. In athird example, the variable value model (e.g., an encoder) can betrained to encode sample characterization values (e.g., functionalproperty feature values) and variable values into the same latent space,wherein variable values are predicted by decoding input samplecharacterization values (e.g., target functional property featurevalues) into target variable values. For example, the variable model canbe trained to encode the sample characterization values for a trainingsample and the corresponding variable values for the same trainingsample to the same point in latent space.

In a second embodiment, the variable value model is trained to outputvariable values for a second prototype based on variable values for afirst prototype and the comparison metric for the first prototyperelative to a target (e.g., where the second prototype variable valuesare calculated to improve the comparison metric relative to the sametarget). The variable values can be output as a specific value and/or adesired change (e.g., based on an output from the correlation model).For example, the variable value model can be trained based on feedbackfrom a comparison between target characterization values andcharacterization values determined based on measurements for a secondtraining sample manufactured using predicted variable values (e.g.,predicted based on the comparison metric for a first training sampleusing the variable value model). An example is shown in FIG. 5A.

However, the variable value model can be otherwise trained.

Optionally, the method can include training a characterization modelS650, which functions to train the characterization model to predictsample characterization values (e.g., given a set of variable values).S650 can be performed before S400, before S500, before S600,concurrently with S600, asynchronously from S600, after a set of sampleshave been characterized with sample characterization values (e.g., usingS100/S200 methods, using S400 methods, etc.), iteratively, periodically(e.g., a new samples are characterized), and/or at any other time.

Training the characterization model can include determining trainingdata for a set of training samples (e.g., characterized samples),wherein the training data can include: variable values, samplecharacterization values (e.g., functional property feature values,classifications, etc.), and/or any other data for each of the settraining samples. The training data can be predetermined (e.g.,predetermined variable values, predetermined sample class, etc.),measured and/or determined based on measurements (e.g., using S100/S200methods), synthetically generated, randomly determined, and/or otherwisedetermined.

In a first variant, the characterization model is trained to predict acharacterization value (e.g., a classification) for a sample based onfunctional property feature values for the sample. In a firstembodiment, the training data can include functional property featurevalues (e.g., training functional property feature values) labeled withassociated sample characterization values (e.g., each set of functionalproperty feature values corresponding to a sample is labeled with aclassification for the sample). The characterization model (e.g., aclassifier) can be trained based on feedback from a comparison betweenpredicted sample characterization values (e.g., a classificationpredicted based on the functional property feature values for a trainingsample using the characterization model) and actual samplecharacterization values for the training sample (e.g., predeterminedclassification); example shown in FIG. 4B. In a specific example,training the characterization model includes clustering the trainingfunctional property feature values (e.g., sets of functional propertyfeature values) into a set of clusters (e.g., based on the associatedlabel), and training the characterization model to select (e.g.,predict) a cluster from the set of clusters based on functional propertyfeature values for a sample. In a second embodiment, the training datacan include unlabeled functional property feature values for thetraining samples. For example, training the characterization model caninclude learning clusters for the training data (e.g., wherein thecharacterization model outputs a cluster identifier and/or a clusterlocation based on a set of functional property feature values).

In a second variant, the characterization model is trained to predict acharacterization value (e.g., functional property feature values, aclassification, etc.) for a sample based on variable values for thesample. For example, the training data can include variable valueslabeled with associated sample characterization values (e.g., measuredfrom a sample prepared using the associated variable values, extractedfrom measurements, a predetermined classification, etc.). Thecharacterization model can be trained based on feedback from acomparison between predicted sample characterization values (e.g.,functional property feature values and/or other sample characterizationvalues predicted based on the variable values using the characterizationmodel) and actual sample characterization values. An example is shown inFIG. 4A.

In any variant, the characterization model can optionally be trainedusing adversarial machine learning methods. The characterization modelcan be trained using adversarial training data for a set of adversarialtraining samples. The adversarial training data can be generated (e.g.,synthetically generated data, measured from physically manufacturedsamples, etc.) such that the characterization model (prior to trainingusing the adversarial training data) mischaracterizes the adversarialtraining samples.

However, the characterization model can be otherwise trained.

The method can optionally include determining interpretability and/orexplainability of a trained model (e.g., characterization model,variable value model, etc.), which can be used to identify errors in thedata, identify ways of improving a model, reduce feature space, increasecomputational efficiency, determine influential features (e.g., afunctional property feature subset) and/or values thereof, determineinfluential variables and/or values thereof, and/or otherwise used.Interpretability and/or explainability methods can include: localinterpretable model-agnostic explanations (LIME), Shapley Additiveexplanations (SHAP), Ancors, DeepLift, Layer-Wise Relevance Propagation,contrastive explanations method (CEM), counterfactual explanation,Protodash, Permutation importance (PIMP), L2X, partial dependence plots(PDPs), individual conditional expectation (ICE) plots, accumulatedlocal effect (ALE) plots, Local Interpretable Visual Explanations(LIVE), breakDown, ProfWeight, Supersparse Linear Integer Models (SLIM),generalized additive models with pairwise interactions (GA2Ms), BooleanRule Column Generation, Generalized Linear Rule Models, TeachingExplanations for Decisions (TED), and/or any other suitable methodand/or approach.

As used herein, “substantially” or other words of approximation can bewithin a predetermined error threshold or tolerance of a metric,component, or other reference, and/or be otherwise interpreted.

Alternative embodiments implement the above methods and/or processingmodules in non-transitory computer-readable media, storingcomputer-readable instructions that, when executed by a processingsystem, cause the processing system to perform the method(s) discussedherein. The instructions can be executed by computer-executablecomponents integrated with the computer-readable medium and/orprocessing system. The computer-readable medium may include any suitablecomputer readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, non-transitorycomputer readable media, or any suitable device. The computer-executablecomponent can include a computing system and/or processing system (e.g.,including one or more collocated or distributed, remote or localprocessors) connected to the non-transitory computer-readable medium,such as CPUs, GPUs, TPUS, microprocessors, or ASICs, but theinstructions can alternatively or additionally be executed by anysuitable dedicated hardware device.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),contemporaneously (e.g., concurrently, in parallel, etc.), or in anyother suitable order by and/or using one or more instances of thesystems, elements, and/or entities described herein. Components and/orprocesses of the following system and/or method can be used with, inaddition to, in lieu of, or otherwise integrated with all or a portionof the systems and/or methods disclosed in the applications mentionedabove, each of which are incorporated in their entirety by thisreference.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method, comprising: measuring a target functionalproperty signal for a target sample; extracting target functionalproperty feature values from the target functional property signal; andusing a trained model, determining a set of manufacturing variablevalues based on the target functional property feature values.
 2. Themethod of claim 1, wherein the prototype functional property signal andthe target functional property signal each comprise a data time series.3. The method of claim 2, wherein the prototype functional propertyfeature values and target functional property feature values are eachextracted using time series decomposition.
 4. The method of claim 1,wherein the functional property features comprise non-semantic features.5. The method of claim 4, wherein the functional property featuresfurther comprise semantic features.
 6. The method of claim 1, whereinthe comparison between the prototype functional property feature valuesand target functional property feature values comprises a distancebetween the prototype functional property feature values and the targetfunctional property feature values.
 7. The method of claim 6, furthercomprising weighting the prototype functional property feature valuesand the target functional property feature values, wherein the distancecomprises a distance between the weighted prototype functional propertyfeature values and the weighted target functional property featurevalues.
 8. The method of claim 1, further comprising measuring a binarycharacteristic of the prototype sample, wherein the set of manufacturingvariable values is determined further based on the binarycharacteristic.
 9. The method of claim 1, wherein training the modelcomprises: measuring a training functional property signal for atraining sample, wherein the training sample is associated with a set oftraining manufacturing variable values; extracting training functionalproperty feature values from the training functional property signal;and training the model to predict the training functional propertyfeature values based on the set of training manufacturing variablevalues.
 10. The method of claim 1, wherein the model comprises anencoder trained to encode functional property feature values andmanufacturing variable values.
 11. The method of claim 1, wherein thetarget functional property signal comprises a measurement for at leastone of: texture, melt, or flavor.
 12. The method of claim 1, wherein thetarget sample comprises a dairy product.
 13. A method, comprising:measuring a functional property signal for a sample, wherein the sampleis manufactured according to a set of variable values; extractingfunctional property feature values from the functional property signal;determining a sample classification for the sample based on thefunctional property feature values, using a trained model; anddetermining a set of updated variable values based on the sampleclassification.
 14. The method of claim 13, wherein the functionalproperty signal comprises a data time series, wherein the functionalproperty feature values are extracted using time series analysis. 15.The method of claim 13, wherein the functional property featurescomprise non-semantic features.
 16. The method of claim 13, wherein themodel is trained using training data comprising training functionalproperty feature values labeled with associated sample classifications.17. The method of claim 13, wherein training the model comprisesclustering training functional property feature values into a set ofclusters, wherein determining the sample classification for the sampleis determined by using the model to select a cluster from the set ofclusters based on the functional property feature values for the sample.18. The method of claim 13, wherein the model is trained usingadversarial machine learning methods.
 19. The method of claim 13,wherein the set of updated variable values are determined usingexplainability methods applied to the trained model.